






"""simple docstring"""





import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_ta import TaTokenizer
else:
  _SCREAMING_SNAKE_CASE   :							Union[str, Any]          =		None


_SCREAMING_SNAKE_CASE   :							Union[str, Any]          =		logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE   :							Optional[int]          =		{'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

_SCREAMING_SNAKE_CASE   :							Optional[Any]          =		{
    '''vocab_file''': {
        '''t5-small''': '''https://huggingface.co/t5-small/resolve/main/spiece.model''',
        '''t5-base''': '''https://huggingface.co/t5-base/resolve/main/spiece.model''',
        '''t5-large''': '''https://huggingface.co/t5-large/resolve/main/spiece.model''',
        '''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/spiece.model''',
        '''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/spiece.model''',
    },
    '''tokenizer_file''': {
        '''t5-small''': '''https://huggingface.co/t5-small/resolve/main/tokenizer.json''',
        '''t5-base''': '''https://huggingface.co/t5-base/resolve/main/tokenizer.json''',
        '''t5-large''': '''https://huggingface.co/t5-large/resolve/main/tokenizer.json''',
        '''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/tokenizer.json''',
        '''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/tokenizer.json''',
    },
}


# TODO(PVP) - this should be removed in Transformers v5
_SCREAMING_SNAKE_CASE   :							int          =		{
    '''t5-small''': 512,
    '''t5-base''': 512,
    '''t5-large''': 512,
    '''t5-3b''': 512,
    '''t5-11b''': 512,
}






class 	a		(   __snake_case						):
    SCREAMING_SNAKE_CASE		:	Dict		=					VOCAB_FILES_NAMES
    SCREAMING_SNAKE_CASE		:	List[str]		=					PRETRAINED_VOCAB_FILES_MAP
    SCREAMING_SNAKE_CASE		:	List[Any]		=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    SCREAMING_SNAKE_CASE		:	str		=					["""input_ids""", """attention_mask"""]
    SCREAMING_SNAKE_CASE		:	Dict		=					TaTokenizer

    SCREAMING_SNAKE_CASE		:	List[int]		=					[]






    def __init__(			self	:				Optional[int]			,		__SCREAMING_SNAKE_CASE	:				Tuple=None			,		__SCREAMING_SNAKE_CASE	:				Any=None			,		__SCREAMING_SNAKE_CASE	:				Optional[int]="</s>"			,		__SCREAMING_SNAKE_CASE	:				Tuple="<unk>"			,		__SCREAMING_SNAKE_CASE	:				Union[str, Any]="<pad>"			,		__SCREAMING_SNAKE_CASE	:				List[Any]=100			,		__SCREAMING_SNAKE_CASE	:				Optional[Any]=None			,		**__SCREAMING_SNAKE_CASE	:				List[Any]			,		)	->						Any:
      # Add extra_ids to the special token list
      if extra_ids > 0 and additional_special_tokens is None:
        lowerCamelCase_       	=		[F'''<extra_id_{i}>''' for i in range(__SCREAMING_SNAKE_CASE   )]
      elif extra_ids > 0 and additional_special_tokens is not None:
        # Check that we have the right number of extra special tokens
        lowerCamelCase_       	=		len(set(filter(lambda __SCREAMING_SNAKE_CASE   : bool('extra_id_' in str(__SCREAMING_SNAKE_CASE   )   )			,		__SCREAMING_SNAKE_CASE   )   )   )
        if extra_tokens != extra_ids:
          raise ValueError(
              F'''Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'''
              ' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'
              ' tokens'   )

      super().__init__(
          __SCREAMING_SNAKE_CASE			,		tokenizer_file=__SCREAMING_SNAKE_CASE			,		eos_token=__SCREAMING_SNAKE_CASE			,		unk_token=__SCREAMING_SNAKE_CASE			,		pad_token=__SCREAMING_SNAKE_CASE			,		extra_ids=__SCREAMING_SNAKE_CASE			,		additional_special_tokens=__SCREAMING_SNAKE_CASE			,		**__SCREAMING_SNAKE_CASE			,		)

      lowerCamelCase_       	=		vocab_file
      lowerCamelCase_       	=		False if not self.vocab_file else True
      lowerCamelCase_       	=		extra_ids






    @staticmethod
    def   UpperCamelCase			(			__SCREAMING_SNAKE_CASE	:				Optional[Any]			,		__SCREAMING_SNAKE_CASE	:				Tuple			,		__SCREAMING_SNAKE_CASE	:				Dict   )	->						int:
      if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
        lowerCamelCase_       	=		TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
        if init_max_model_length is not None and init_max_model_length != max_model_length:
          return init_max_model_length
        elif init_max_model_length is None:
          warnings.warn(
              'This tokenizer was incorrectly instantiated with a model max length of'
              F''' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'''
              ' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'
              ' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'
              F''' {pretrained_model_name_or_path} automatically truncating your input to'''
              F''' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'''
              F''' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'''
              ' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'
              ' instantiate this tokenizer with `model_max_length` set to your preferred value.'			,		__SCREAMING_SNAKE_CASE			,		)

      return max_model_length






    def   UpperCamelCase			(			self	:				Dict			,		__SCREAMING_SNAKE_CASE	:				str			,		__SCREAMING_SNAKE_CASE	:				Optional[str] = None   )	->						Tuple[str]:
      if not self.can_save_slow_tokenizer:
        raise ValueError(
            'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
            'tokenizer.'   )

      if not os.path.isdir(__SCREAMING_SNAKE_CASE   ):
        logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''   )
        return
      lowerCamelCase_       	=		os.path.join(
          __SCREAMING_SNAKE_CASE			,		(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']   )

      if os.path.abspath(self.vocab_file   ) != os.path.abspath(__SCREAMING_SNAKE_CASE   ):
        copyfile(self.vocab_file			,		__SCREAMING_SNAKE_CASE   )
        logger.info(F'''Copy vocab file to {out_vocab_file}'''   )

      return (out_vocab_file,)






    def   UpperCamelCase			(			self	:				List[Any]			,		__SCREAMING_SNAKE_CASE	:				List[int]			,		__SCREAMING_SNAKE_CASE	:				Optional[List[int]] = None   )	->						List[int]:
      lowerCamelCase_       	=		token_ids_a + [self.eos_token_id]
      if token_ids_a is None:
        return self.prefix_tokens + token_ids_a
      else:
        lowerCamelCase_       	=		token_ids_a + [self.eos_token_id]
        return self.prefix_tokens + token_ids_a + token_ids_a






    def   UpperCamelCase			(			self	:				Dict			,		__SCREAMING_SNAKE_CASE	:				List[int]			,		__SCREAMING_SNAKE_CASE	:				Optional[List[int]] = None   )	->						List[int]:
      lowerCamelCase_       	=		[self.eos_token_id]

      if token_ids_a is None:
        return len(token_ids_a + eos   ) * [0]
      return len(token_ids_a + eos + token_ids_a + eos   ) * [0]






    def   UpperCamelCase			(			self	:				Tuple   )	->						Tuple:
      return list(
          set(filter(lambda __SCREAMING_SNAKE_CASE   : bool(re.search(r'<extra_id_\d+>'			,		__SCREAMING_SNAKE_CASE   )   ) is not None			,		self.additional_special_tokens   )   )   )






    def   UpperCamelCase			(			self	:				Optional[Any]   )	->						str:
      return [self.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE   ) for token in self.get_sentinel_tokens()]












"""simple docstring"""





def 	lowerCamelCase__			(				_lowerCamelCase			:						float						,							_lowerCamelCase			:						float					) ->							float:

  if density <= 0:
    raise ValueError('Impossible fluid density'					)
  if bulk_modulus <= 0:
    raise ValueError('Impossible bulk modulus'					)

  return (bulk_modulus / density) ** 0.5


if __name__ == "__main__":
  import doctest

  doctest.testmod()




